---
title: "Customer Sentiments Analysis Dashboard"
output: 
  flexdashboard::flex_dashboard:
    orientation: columns
    vertical_layout: fill
    runtime: shiny
    logo: Logo.png
    storyboard: true
    social: menu
    source: embed
    theme: spacelab
---

```{r setup, include=FALSE}
library(flexdashboard)
require(tidyverse)
require(httr)
require(jsonlite)
require(rlist)
require(stringr)
require(sqldf)
require(ggplot2)
require(beeswarm)
require(googleway)
require(highcharter)
require(quantmod)
require(leaflet)
require(shiny)
require(bslib)
require(plotly)
require(dygraphs)
require(treemap)
require(DT)
require(romato)

df_keys <- read.csv(file = '/Users/Abs/Documents/GitHub/R Sample Codes/R-Codes/keys.csv')

df_yelp <- df_keys[grepl('\\Yelp', df_keys$name),]

df_zomato <- df_keys[grepl('\\Zomato', df_keys$name),]

client_id <- as.character(df_yelp$id)
client_secret <- as.character(df_yelp$key)
res <- POST("https://api.yelp.com/oauth2/token",
            body = list(grant_type = "client_credentials",
                        client_id = client_id,
                        client_secret = client_secret))

token <- content(res)$access_token

yelp <- "https://api.yelp.com"
location <- "Chicago, IL"
categories <- NULL
limit <- 50
radius <- 8800
term <- "Pizza"
url <- modify_url(yelp, path = c("v3", "businesses", "search"),
                  query = list(term = term, location = location, 
                               limit = limit,
                               radius = radius))
res <- GET(url, add_headers('Authorization' = paste("bearer", client_secret)))

results <- content(res)

yelp_httr_parse <- function(x) {
  
  parse_list <- list(id = x$id, 
                     name = x$name, 
                     rating = x$rating, 
                     review_count = x$review_count, 
                     latitude = x$coordinates$latitude, 
                     longitude = x$coordinates$longitude, 
                     address1 = x$location$address1, 
                     city = x$location$city, 
                     state = x$location$state, 
                     distance = x$distance)
  
  parse_list <- lapply(parse_list, FUN = function(x) ifelse(is.null(x), "", x))
  
  df <- tibble(id=parse_list$id,
                   name=parse_list$name, 
                   rating = parse_list$rating, 
                   review_count = parse_list$review_count, 
                   latitude=parse_list$latitude, 
                   longitude = parse_list$longitude, 
                   address1 = parse_list$address1, 
                   city = parse_list$city, 
                   state = parse_list$state, 
                   distance= parse_list$distance)
  df
}

results_list <- lapply(results$businesses, FUN = yelp_httr_parse)

business_data <- do.call("rbind", results_list)


#write.table(business_data, "/Users/Abs/Documents/GitHub/R Sample Codes/R-Codes/analysis.csv",
#            append = TRUE,
#            sep = ",",
#            col.names = FALSE,
#            row.names = FALSE,
#            quote = FALSE)

df_new <- read.csv(file = '/Users/Abs/Documents/GitHub/R Sample Codes/R-Codes/analysis.csv')

my_data <- as_tibble(df_new)

df_good <- my_data %>% filter(rating > 3.5)

zmt <- zomato$new(api_key = as.character(df_zomato$key))

pizza_blr <- zmt$search(query = "Pizza", lat = 12.972442, lon = 77.580643)

pizza_aus <- zmt$search(query = "Pizza", lat = -33.865143, lon = 151.209900)
```

### Location Markers across Midwest for Domino's Pizza Store featuring Yelp Ratings

```{r}
leaflet(df_new) %>% 
addTiles() %>%
addCircles(lng =df_new$longitude, lat = df_new$latitude, weight = 2,
             radius = df_new$review_count*1, popup=paste(df_new$name," - ",df_new$address1,",",df_new$city," , rating :",df_new$rating," review : ",df_new$review_count)
  )

```

*** 
Geo Spatial Inference Points :

- Pizza restaurants analysis for 3 cities : Chicago, Detroit & Ann Arbor
- Jolly Pumpkin Cafe at Ann Arbor has 902 reviews with 4 rating
- Chicago's Pequod's Pizzeria has 335 reviews with 4 rating

\
 
References :

- [Yelp API](https://www.yelp.com/developers/documentation/v3)
- [leaflet visual](http://rstudio.github.io/leaflet/shapes.html)


### Overall distribution of customer reviews across Ann Arbor Restaurants

```{r}
df_annarbor <- df_good[grepl('\\Ann Arbor', df_good$city),]
hchart(df_annarbor, "scatter", hcaes(x = review_count, y = rating, z = review_count, group = name))
```

*** 
Ann Arbor Pizza Restaurants Inference Points :

- Jolly Pumpkin Cafe at Ann Arbor has 902 reviews with 4 rating
- NeoPapalis has 313 reviews, however has 4.5 rating

\
 
References :

- [Yelp API](https://www.yelp.com/developers/documentation/v3)
- [Highcharter](https://jkunst.com/highcharter/)


### Overall distribution of customer reviews across Detroit Restaurants

```{r}
df_detroit <- df_good[grepl('\\Detroit', df_good$city),]
hchart(df_detroit, "scatter", hcaes(x = review_count, y = rating, z = review_count, group = name))
```

*** 
Detroit's Pizza Restaurants Inference Points

- Detroit's Supino Pizzeria has 882 reviews with 4.5 rating, followed by Giovanni Ristorante
- Ottawa Via is second most in terms of reviews : 664 , with rating 4

\
 
References :

- [Yelp API](https://www.yelp.com/developers/documentation/v3)
- [Highcharter](https://jkunst.com/highcharter/)


### Overall distribution of customer reviews across Chicago Restaurants

```{r}
df_chicago <- df_good[grepl('\\Chicago', df_good$city),]
hchart(df_chicago, "scatter", hcaes(x = review_count, y = rating, z = review_count, group = name))
```

*** 
Chicago's Pizza Restaurants Inference Points

- Chicago's Pequod's Pizzeria has 6k reviews with 4 rating, followed by Lou Molnati's Pizzeria
- Coalfire Pizza has most rating with 4.5 with 1k reviews

\
 
References :

- [Yelp API](https://www.yelp.com/developers/documentation/v3)
- [Highcharter](https://jkunst.com/highcharter/)


### Location Markers across Bangalore for Pizza Delivery Store featuring Zomato Ratings

```{r}

leaflet(pizza_blr) %>% 
addTiles() %>%
addCircles(lng =as.numeric(pizza_blr$longitude), lat = as.numeric(pizza_blr$latitude), weight = 2,
             radius = ~sqrt(as.numeric(pizza_blr$votes))*7, popup=paste(pizza_blr$name,":",pizza_blr$locality," , rating :",pizza_blr$aggregate_rating," votes : ",pizza_blr$votes)
  )

```

*** 
Inference Points :

- BTM Layout in Bangalore is rocking it ! With 4.2 rating and 15k votes.
- Soulspace in Marthalli needs to up their game, 3.1 rating around 5k votes.

\
 
References :

- [Zomato API](https://developers.zomato.com)
- [leaflet visual](http://rstudio.github.io/leaflet/shapes.html)




### Overall distribution of Reviews across different localities in Bangalore, India with average customer rating

```{r}
hchart(pizza_blr, "scatter", hcaes(x = as.numeric(votes), y = as.numeric(aggregate_rating), z = as.numeric(aggregate_rating), group = locality))
```

*** 
Inference Points :

- BTM Layout in Bangalore is rocking it ! With 4.2 rating and 15k votes.
- Soulspace in Marthalli needs to up their game, 3.1 rating around 5k votes.

\
 
References :

- [Zomato API](https://developers.zomato.com)
- [Highcharter](https://jkunst.com/highcharter/)


### Location Markers across Sydney Australia for Pizza Delivery Store featuring Zomato Ratings

```{r}
leaflet(pizza_blr) %>% 
addTiles() %>%
addCircles(lng =as.numeric(pizza_aus$longitude), lat = as.numeric(pizza_aus$latitude), weight = 2,
             radius = ~sqrt(as.numeric(pizza_aus$votes))*7, popup=paste(pizza_aus$name,":",pizza_aus$locality," , rating :",pizza_aus$aggregate_rating," votes : ",pizza_aus$votes)
  )

```

*** 
Inference Points :

- Sydney Olympic Park seems to be leading ratings 3.4 with 18 reviews.
- Store in Mascot was rated at 2.5 with 22 reviews.

\
 
References :

- [Zomato API](https://developers.zomato.com)
- [leaflet visual](http://rstudio.github.io/leaflet/shapes.html)


### Overall distribution of Reviews across different localities in Sydney, Australia with average customer rating

```{r}
hchart(pizza_aus, "scatter", hcaes(x = as.numeric(votes), y = as.numeric(aggregate_rating), z = as.numeric(aggregate_rating), group = locality))
```


### Detail Yelp Data around reviews and ratings for stores across Midwest Metro City Areas

```{r}
datatable(df_good)
```